Systems and methods for customized navigation menus

ABSTRACT

Systems and methods for generating personalized navigation menus are disclosed. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving account data associated with a plurality of accounts and receiving navigation menu data associated with the accounts. The operations may include training a model based on the account data and the navigation menu data. The operations may include receiving a request associated with a user and receiving user account data associated with the user. The operations may include generating, using the model, a navigation menu based on the request and the user account data. The operations may include providing the navigation menu.

BACKGROUND

Conventional systems and methods for providing navigation menus in aninterface typically consist of providing set of default options to users(e.g., visitors and/or callers). Navigation menus may include a seriesof prompts (e.g., questions, labels, information, or other data), andusers may provide responses to these prompts. For example, a navigationmenu may be provided to user (e.g., a consumer, a member, an accountholder) making a contact by phone, visiting a website, or using an app.Typically, a user initiates a request for a particular desired outcome(e.g., checking an account status) but may need to navigate throughmultiple prompts irrelevant to the desired outcome. This cumbersome andinefficient process wastes time and wastefully consumes resources (e.g.,bandwidth, processing power, etc.).

Although some solutions have been proposed, conventional solutions lackefficiency and effectiveness. One conventional approach includesallowing a user to configure preferences for navigation menus inadvance. However, this approach is limited at least because it requiresactive user participation and has limited flexibility. After configuringpreferences, a user may later initiate a request for a desired outcomethat requires navigation through multiple, irrelevant prompts. Anotherconventional approach is to use systems that may allow a user reachparticular prompt or set of prompts without navigating through allintermediate prompts (e.g., an Interactive Voice Response (IVR) system).However, this approach is inefficient because it requires user input toreach a desired outcome and typically requires rule-based programminginvolving the prompts.

Therefore, in view of the shortcomings and problems with conventionalapproaches to providing navigation menus, there is a need forunconventional approaches to providing navigation menus that areefficient, effective, and customizable and anticipate a desired outcome.

SUMMARY

The disclosed embodiments provide unconventional methods and systems forgenerating navigation menus, including interactive voice response (IVR)menus. As compared to conventional solutions, the embodiments includeefficient, effective and customizable methods of generating navigationmenus, providing prompts, and routing signals. In some embodiments,systems and methods may anticipate a desired outcome and dynamicallygenerate a navigation menu. Some embodiments may include machinelearning systems and methods in addition to or instead of rule-basedprogramming methods to generate navigation menus.

Consistent with the present embodiments, a system for generatingnavigation menus is disclosed. The system may include one or more memoryunits storing instructions and one or more processors configured toexecute the instructions to perform operations. The operations mayinclude receiving account data associated with a plurality of accountsand receiving navigation menu data associated with the accounts. Theoperations may include training a model based on the account data andthe navigation menu data. The operations may include receiving a requestassociated with a user and receiving user account data associated withthe user. The operations may include generating, using the model, anavigation menu based on the request and the user account data. Theoperations may include providing the navigation menu.

Consistent with the present embodiments, a method for generatingnavigation menus is disclosed. The method may include receiving accountdata associated with a plurality of accounts and receiving navigationmenu data associated with the accounts. The method may include traininga model based on the account data and the navigation menu data. Themethod may include receiving a request associated with a user andreceiving user account data associated with the user. The method mayinclude generating, using the model, a navigation menu based on therequest and the user account data. The method may include providing thenavigation menu.

Consistent with other disclosed embodiments, non-transitory computerreadable storage media may store program instructions, which areexecuted by at least one processor device and perform any of the methodsdescribed herein.

The disclosed systems and methods may be implemented using a combinationof conventional hardware and software as well as specialized hardwareand software, such as a machine constructed and/or programmedspecifically for performing functions associated with the disclosedmethod steps. The foregoing general description and the followingdetailed description are exemplary and explanatory only and are notrestrictive of the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate several embodiments and, togetherwith the description, serve to explain the disclosed principles. In thedrawings:

FIG. 1 depicts an exemplary system for generating navigation menus,consistent with disclosed embodiments.

FIG. 2 depicts exemplary navigation menus, consistent with disclosedembodiments.

FIG. 3 depicts an exemplary navigation-menu system, consistent withdisclosed embodiments.

FIG. 4 depicts an exemplary user device, consistent with disclosedembodiments.

FIG. 5 depicts an exemplary process for training a model to generate anavigation menu, consistent with disclosed embodiments.

FIG. 6 depicts an exemplary process for generating a navigation menu,consistent with disclosed embodiments.

FIG. 7 depicts an exemplary process to view, create, update, or delete anavigation-menu rule, consistent with disclosed embodiments.

FIG. 8 depicts an exemplary process for activating a user account,consistent with disclosed embodiments.

DESCRIPTION OF THE EMBODIMENTS

Consistent with disclosed embodiments, systems and methods to generatenavigation menus are disclosed. Systems and methods may involvenavigation menus of various types, including interactive voice response(IVR) menus (e.g., a customer-service menu provided to a caller),displayed navigation menus (e.g., website menus, app menus,user-interface menus), and/or other navigation menus.

Embodiments consistent with the present disclosure may include accountdata. The account data may be associated with a user (i.e., user-accountdata) and/or more than one user. In some embodiments, an account may bea financial account (e.g., a credit card or bank), an insurance account,a healthcare-provider account, a utility account (e.g., a telephone,mobile phone, gas, electric, or water account), a user account, aservice account (e.g., an automobile service account), a merchantaccount (e.g., an online store account), an email account, a messagingaccount, a membership account, an educational account, and/or any otheraccount. Account data may include data related to an account status(e.g., a classification of the account, a balance, a number of messages,data used, a length of time an account has been active, etc.) and/or anaccount event (e.g., a call, a transaction, an order, a message sent orreceived, a download or upload, a synchronization of a file or message,a change of an account setting, etc.). Account data may includetransaction data (e.g., payment data, charge data, and/or deposit data),healthcare data, service data, messaging data, purchase data,educational data, utility data, electricity data, water data, call data,Short Message Service (SMS) data, messaging data, email data, and/orother data.

Embodiments consistent with the present disclosure may include requestdata. Request data may be related to a request for an account service.The request may be associated with a user (i.e., user-request data)and/or an account. For example, request data may include a desiredoutcome, including to check an account status, schedule an event, reportfraud, change an address, determine whether an anticipated account eventoccurred, etc. Request data may include phone number, an IP address,location data, time data (e.g., the time of a request for service, theduration of a service call, etc.), user-satisfaction data (e.g., dataindicating whether a service issue was resolved), or other dataassociated with a request for an account service. Request data mayinclude data associated with an IVR process (e.g., audio data comprisingresponses to prompts).

Embodiments consistent with the present disclosure may includenavigation menus comprising navigation-menu data. Navigation-menu datamay include data specifying a configuration of prompts and/or promptdata. Prompt data may include audio data, text data, numeric data, imagedata, video data, IVR data, and/or any other data. For example, promptdata may include a statement (e.g., “press 1 for customer service”), alabel (e.g., “Pharmacy Department”), a question (e.g., “are you callingto check your account balance?”), information (e.g., “your nextscheduled appointment is Oct. 8, 2020”), and/or any other data.Navigation-menu data may include prompt selection data (e.g., previouslyselected prompts, statistics relating to selected prompts for one ormore requests and/or one or more users, etc.). In some embodiments,prompts of navigation-menus may be organized into one or more levels(e.g., Level 1, Level 2, and Level 3). Within a level, an order ofprompts flows from top to bottom of the diagram. In some embodiments,prompts may be organized according to an order that influences the orderin which prompts may be provided (e.g., displayed or played). As one ofskill in the art will appreciate, navigation-menus may include promptsconfigured according to any network structure (e.g., node-edgestructure). Examples navigation-menus provided herein are illustrativeonly and not limiting on the embodiments.

Embodiments consistent with the present disclosure may includenavigation-menu rules. A navigation-menu rule may include a logicalexpression that specifies an element of a configuration of a navigationmenu when a condition satisfied. For example, the condition may includedetermining whether request data is associated with a phone number, alocation, an IP address, a time of day, a user, etc. In someembodiments, the condition may involve account data (e.g., determiningwhether a deposit is above a threshold amount) or an account status(e.g., determining whether a balance is past due). In some embodiments,an element of a configuration of a navigation menu may includespecifying a predetermined prompt at a predetermined position of theconfiguration (e.g., an option to speak to an operator must be presentedat the first position at each level of a navigation menu). Anavigation-menu rule may include a user preference (e.g., a languagepreference). As one of skill in the art will appreciate, examples ofnavigation-menu rules provided herein are illustrative only and othernavigation-menu rules are possible.

Embodiments consistent with the present disclosure may include activitydata. Activity data may include location data, cookie data, websitehistory data, viewing history data (e.g., online video streamingactivity), shopping history data, transaction data, internet searchdata, social media data, or other activity data. Activity data may beassociated with a user (e.g. user-activity data), more than one user, anaccount, and/or more than one account.

Reference will now be made in detail to exemplary embodiments, examplesof which are illustrated in the accompanying drawings and disclosedherein. Wherever convenient, the same reference numbers will be usedthroughout the drawings to refer to the same or like parts. Thedisclosed embodiments are described in sufficient detail to enable thoseskilled in the art to practice the disclosed embodiments. It is to beunderstood that other embodiments may be utilized and that changes maybe made without departing from the scope of the disclosed embodiments.Thus, the materials, methods, and examples are illustrative only and arenot intended to be necessarily limiting.

FIG. 1 depicts exemplary system 100 for generating navigation menus,consistent with disclosed embodiments. As shown, system 100 may includea user device 102, a navigation-menu system 104, an account system 106,a third-party system 108, a database 110, and a speech-recognitionsystem 112. Components of system 100 may be connected to each otherthrough a network 114.

In some embodiments, aspects of system 100 may be implemented on one ormore cloud services designed to generate (“spin-up”) one or moreephemeral container instances in response to event triggers, assign oneor more tasks to a container instance, and terminate (“spin-down”) acontainer instance upon completion of a task. By implementing methodsusing cloud services, disclosed systems may efficiently provisionresources based on demand and provide security advantages because theephemeral container instances may be closed and destroyed uponcompletion of a task. That is, the container instances do not permitaccess from outside using terminals or remote shell tools like SSH, RTP,FTP, or CURL, for example. Further, terminating container instances mayinclude destroying data, thereby protecting sensitive data. Destroyingdata can provide security advantages because it may involve permanentlydeleting data (e.g., overwriting data) and associated file pointers.

As will be appreciated by one skilled in the art, the components ofsystem 100 can be arranged in various ways and implemented with anysuitable combination of hardware, firmware, and/or software, asapplicable. For example, as compared to the depiction in FIG. 1, system100 may include a larger or smaller number of user devices,navigation-menu systems, account systems, third-party systems,databases, speech-recognition systems, and/or networks. In addition,system 100 may further include other components or devices not depictedthat perform or assist in the performance of one or more processes,consistent with the disclosed embodiments. The exemplary components andarrangements shown in FIG. 1 are not intended to limit the disclosedembodiments.

User device 102 may include one or more memory units and one or moreprocessors configured to perform operations consistent with disclosedembodiments. In some embodiments, user device 102 may include hardware,software, and/or firmware modules. User device 102 may be a mobiledevice, a tablet, a personal computer, a terminal, a kiosk, a server, aserver cluster, a cloud service, a storage device, a specialized deviceconfigured to perform methods according to disclosed embodiments, or thelike. User device 102 is disclosed in greater detail below (in referenceto FIG. 4).

Navigation-menu system 104 may include a computing device, a computer, aserver, a server cluster, a plurality of clusters, and/or a cloudservice, consistent with disclosed embodiments. Navigation-menu system104 may include one or more memory units and one or more processorsconfigured to perform operations consistent with disclosed embodiments.Navigation-menu system 104 may include computing systems configured togenerate, receive, retrieve, store, and/or provide navigation menus,consistent with disclosed embodiments. Navigation-menu system 104 mayinclude computing systems configured to generate and train models,consistent with disclosed embodiments. Navigation-menu system 104 may beconfigured to receive data from, retrieve data from, and/or transmitdata to other components of system 100 and/or computing componentsoutside system 100 (e.g., via network 114). In some embodiments (notshown), navigation-menu system 104 may be a component of anothercomponent or system of system 100 (e.g., account system 106).Navigation-menu system 104 is disclosed in greater detail below (inreference to FIG. 3).

Account system 106 may include one or more memory units and one or moreprocessors configured to perform operations consistent with disclosedembodiments. Account system 106 may include a computing device, acomputer, a server, a server cluster, a plurality of clusters, and/or acloud service, consistent with disclosed embodiments. Account system 106may be configured to generate and train models, consistent withdisclosed embodiments. Account system 106 may be configured to receivedata from, retrieve data from, and/or transmit data to other componentsof system 100 and/or computing components outside system 100 (e.g., vianetwork 114). In some embodiments (not shown), account system 106 may bea component of navigation-menu system 104.

Account system 106 may be configured to manage data (e.g., monitor,analyze, store, and/or provide data), consistent with disclosedembodiments. The data may include account data, request data,navigation-menu data, and/or navigation-menu rules (as previouslydescribed). In some embodiments, account system 106 may be configured togenerate and/or provide navigation menus. In some embodiments, accountsystem 106 may be configured to generate and/or provide prompts. In someembodiments, account system 106 may be configured to conduct an IVRprocess (e.g., in coordination with speech-recognition system 112 and/ornavigation-menu system 104).

Account system 106 may be configured to route signals, consistent withdisclosed embodiments. For example, account system 106 may be configuredto route call signals, internet connection signals, and/or othersignals. As an example, account system 106 may be configured to route acall to a department and/or to route a website visitor to an internetpage.

Third-party system 108 may be configured to manage activity data (e.g.,monitor, analyze, store, and/or provide activity data) associated with auser or an account, consistent with disclosed embodiments. Third-partysystem 108 may include one or more memory units and one or moreprocessors configured to perform operations consistent with disclosedembodiments. Third-party system 108 may include a computing device, acomputer, a server, a server cluster, a plurality of clusters, and/or acloud service, consistent with disclosed embodiments. Third-party system108 may be configured to receive data from, retrieve data from, and/ortransmit data to other components of system 100 and/or computingcomponents outside system 100 (e.g., via network 114). In someembodiments, the activity data may include at least one of locationdata, cookie data, website history data, viewing history data (e.g.,online video streaming activity), shopping history data, transactiondata, internet search data, social media data, or other activity data.In some embodiments, third-party system 108 may include a social mediasystem.

Database 110 may include one or more databases configured to store datafor use by system 100, consistent with disclosed embodiments. Database110 may include a cloud-based database (e.g., AMAZON WEB SERVICESRELATIONAL DATABASE SERVICE) or an on-premises database. Database 110may include request data, model data (e.g., model parameters), activitydata, account data, navigation-menu data, and/or navigation-menu ruledata, consistent with disclosed embodiments. Database 110 may includedata received from one or more components of system 100 and/or computingcomponents outside system 100 (e.g., via network 114).

Speech-recognition system 112 may be configured to identify speech in anaudio signal and/or to convert speech to text, consistent with disclosedembodiments. Speech-recognition system 112 may include one or morememory units and one or more processors configured to perform operationsconsistent with disclosed embodiments. Speech-recognition system 112 mayinclude a computing device, a computer, a server, a server cluster, aplurality of clusters, and/or a cloud service, consistent with disclosedembodiments. In some embodiments, speech-recognition system 112 may beprovided as a service (e.g., MICROSOFT COGNITIVE SERVICES).Speech-recognition system 112 may be configured to generate and trainmodels, consistent with disclosed embodiments. Speech-recognition system112 may be configured to receive data from, retrieve data from, and/ortransmit data to other components of system 100 and/or computingcomponents outside system 100 (e.g., via network 114). In someembodiments (not shown), speech-recognition system 112 may be acomponent of another component or system of system 100, for examplenavigation-menu system 104 or account system 106. In some embodiments,speech-recognition system is configured for IVR processes.

Speech-recognition system 112 may include programs for voice and speechrecognition processes. Speech-recognition system 112 may include voicerecognition algorithms. Speech-recognition system 112 may includealgorithms to convert speech to text and/or identify a speaker.Speech-recognition system 112 may include a machine learning model.Speech-recognition system 112 may include at least one of neural networkmodel, a deep learning model, a conversational model, a supervisedmodel, a hidden Markov model, a self-learning model, a discriminativelearning model, a Bayesian learning model, a structured sequencelearning model, an adaptive learning model, a statistical model, and/oranother machine learning model. In some embodiments, speech-recognitionsystem 112 is configured to train machine learning models.

At least one of user device 102, navigation-menu system 104, accountsystem 106, third-party system 108, database 110, and speech-recognitionsystem 112 may connect to network 114. Network 114 may be a publicnetwork or private network and may include, for example, a wired orwireless network, including, without limitation, a Local Area Network, aWide Area Network, a Metropolitan Area Network, an IEEE 1002.11 wirelessnetwork (e.g., “Wi-Fi”), a network of networks (e.g., the Internet), aland-line telephone network, or the like. Network 114 may be connectedto other networks (not depicted in FIG. 1) to connect the various systemcomponents to each other and/or to external systems or devices. In someembodiments, network 114 may be a secure network and require a passwordto access the network.

FIG. 2 depicts exemplary navigation menus, consistent with disclosedembodiments. FIG. 2 presents a first navigation menu 210 with a firstconfiguration (i.e., organization of prompts) and a second navigationmenu 220 with a second configuration. In some embodiments,navigation-menu system 104 may generate and/or provides navigation menu210 and/or navigation menu 220. Generating a navigation menu may includeselecting prompts to include in the navigation menu. Generating anavigation menu may include setting a configuration of the navigationmenu (i.e., organizing prompts into one or more levels and/or setting anorder of prompts), consistent with disclosed embodiments.

Providing a navigation menu may include displaying, playing, and/ortransmitting navigation-menu data. For example, providing a navigationmenu may include providing a prompt (e.g., displaying, playing, and/ortransmitting a prompt), consistent with disclosed embodiments. In someembodiments, navigation-menu system 104 may transmit a prompt to userdevice 102. For example, navigation-menu system 104 may transmit aprompt in response to a received call from a user device 102. Aninterface may display prompts (e.g., a display of user device 102). Aspeaker may play prompts (e.g., a speaker of user device 102). Asanother example, navigation-menu system 104 may transmit a prompt toaccount system 106 (e.g., in response to data received from accountsystem 106). For example, account system 106 may receive a request(e.g., a website request, a call, an SMS message) and transmit requestdata to navigation-menu system 104 based on the request. In someembodiments, providing a prompt may include routing a signal. A signalmay include a call signal, an internet connection signal, and/or anyother signal.

As shown, navigation menus of FIG. 2 include a plurality of promptslabelled as “A” through “T” and “Z”. Navigation menus may organizeprompts into a Level 1, a Level 2, and a Level 3, as shown in FIG. 2.Within a level, an order of prompts may flow from top to bottom of thediagram. In some embodiments, the order of prompts may determine theorder in which prompts are played over a speaker. In some embodiments,the order of prompts may determine the order in which prompts appear ina list or the order in which prompts are displayed in a sequence. Insome embodiments, prompts of only one level may be provided until aprompt is selected. In FIG. 2, lines between prompts represent possiblenavigation pathways through the prompts of navigation menus 210 and 220.A navigation pathway includes a possible sequence of selections througha navigation menu. In some embodiments, a prompt may be provided basedon a selection of another prompt.

For example, navigation-menu system 104 may receive data (e.g., requestdata) and provide prompts in Level 1 of navigation menu 210 in response.For example, the system may provide prompts A, B, and C based onreceived request data. Navigation-menu system 104 may receive additionalrequest data and select prompt A based on the additional request data.The system may provide prompts D and E based on the selection of promptA. In some embodiments, prompts may be selected based on data receivedfrom a component of system 100. In some embodiments, navigation-menusystem 104 may receive data from another component of system 100, acomputing component outside system 100, and/or a connected componentsuch as a keyboard, a touchscreen, a mouse, a microphone, a dial, aknob, a switch, and/or another input device (e.g., I/O devices 320,discussed in further detail in relation to FIG. 3 below).

As shown in relation to navigation menu 210, navigation-menu system 104may provide Level 1 prompts in the following order: prompt A, followedby prompt B, then prompt C. If prompt B is selected, the system mayprovide Level 2 prompts of navigation menu 210 that are associated withthe prompt B. That is, the system may provide prompt F, then prompt G,then prompt H. If prompt F is selected, the system may provide Level 3prompts of navigation menu 210 that are associated with prompt F. Thatis, the system may provide prompt M, then prompt N, then prompt O.

In some embodiments, navigation menu 220 may be another configuration ofnavigation menu 210. For example, navigation menu 210 may be a defaultnavigation menu and navigation menu 220 may be a customized navigationmenu. Alternatively, navigation menu 210 may be a first customizednavigation menu and navigation menu 220 may be a second customizednavigation menu. As yet another example, navigation menu 210 may be acustomized navigation menu and navigation menu 220 may be a defaultnavigation menu.

To explain in further detail, navigation menu 220 may include prompts ofnavigation menu 210 (e.g., prompts A through T, as shown). In addition,as compared to navigation menu 210, navigation menu 220 may includedifferent prompts. For example, navigation menu 220 includes a prompt Znot found in navigation menu 210. Prompt Z may be included based on anavigation-menu rule (i.e., a logical expression that specifies anelement of a configuration of a navigation menu when a conditionsatisfied). Prompt Z may be included based on account data, activitydata, request data, and/or navigation-menu data. For example, althoughthe system may not normally include prompt Z in response to a call, themodel may offer prompt Z based on unusual account data (e.g., anunusually large transaction, a past-due medical appointment, etc.).

Navigation menu 220 organizes prompts in a Level 1, a Level 2, and aLevel 3. As compared to navigation menu 210, navigation menu 220 mayinclude a different order of prompts at any level. For example, Level 1of navigation menu 210 includes prompts A, B and C in that order, whileLevel 1 of navigation menu 220 includes prompts Q, D, P, B, A, and C inthat order. Navigation menu 220 may preserve associations of navigationmenu 210 between prompts at different levels. As an example, prompt B isassociated with the same Level 2 prompts (F, G, and H) in navigationmenu 210 and in navigation menu 220, but the order of these Level 2prompts is not the same for navigation menu 210 as for navigation menu220

In some embodiments, navigation-menu system 104 may generate navigationmenu 210 and/or navigation menu 220 based on account data (e.g., depositdata), request data (e.g., a phone number), activity data (e.g., websitevisit history), navigation-menu data (e.g., previously selectedprompts), and/or navigation-menu rules (e.g., a preferred prompt). Insome embodiments, navigation-menu system 104 may generate navigationmenus using a model.

As an example, navigation-menu system 104 may generate navigation menu210 when request data is associated with a first location and generatenavigation menu 220 when request data is associated with a secondlocation. The first location may be a place of business and the requestdata may include a business phone number, an Internet Protocol (IP)address associated with a place of business, and/or location dataassociated with a place of business (e.g., Global Positioning System(GPS) data). The second location may be a residence, and the requestdata may include a residential phone number, an IP address associatedwith a residence, and/or location data associated with a residence.

In the example, account system 106 may be a financial account system andaccount holders may frequently call or login to account system 106 froma place of business to check an account balance and receive informationabout recent transactions. However, account holders may frequently callor login from a residence to schedule payments, receive informationabout recent transactions, and dispute transactions. Navigation-menusystem 104 may train a model (e.g., a machine learning model) togenerate navigation menu 210 based on request data associated with aplace of business and generate navigation menu 220 based on request dataassociated with a residence.

Advantageously, navigation-menu system 104 generates customizednavigation menus configured for efficient navigation. In the previousexample, prompt A may include account balance information, prompt D mayinclude recent transaction data, prompt Q may payment information, andprompt P may include dispute information. As shown, navigation menu 210is configured to provide rapid navigation pathways at least to prompts Aand D. Accordingly, in the example, users calling from a place ofbusiness may quickly navigate to check an account balance and recenttransactions. As shown, navigation menu 220 is configured to providerapid navigation pathways at least to prompts Q, D, and P. Accordingly,in the example, users calling from a residence may quickly navigate toschedule payments, check recent transactions, and dispute transactions.

FIG. 2 is provided for purposes of illustration only and is not intendedto be limiting on the embodiments. Embodiments may include navigationmenus that differ from the navigation menus depicted in FIG. 2. Forexample, navigation menus of the embodiments may include greater orfewer prompts, levels, and/or navigation pathways as compared to theprompts, levels and navigation pathways depicted in FIG. 2. While theexamples provided herein may include navigation pathways through simpleparent-child relationships between prompts, navigation menus of theembodiments may include complex relationships between prompts thatinclude multiple branching and converging structures, for example. Asone of skill in the art will appreciate, navigation-menus of theembodiments may include prompts configured according to any networkstructure (e.g., any root, parent, leaf, child, or other structureaccording to tree topology, bus topology, star topology, and/or anyother topology).

FIG. 3 depicts an exemplary navigation-menu system 104, consistent withdisclosed embodiments. Navigation-menu system 104 may comprise acomputing device, a computer, a server, a server cluster, a plurality ofclusters, and/or a cloud service, consistent with disclosed embodiments.As shown, navigation-menu system 104 may include one or more processors310, one or more I/O devices 320, and one or more memory units 330. Insome embodiments, some or all components of navigation-menu system 104may be hosted on a device, a computer, a server, a cluster of servers,or a cloud service. In some embodiments, navigation-menu system 104 maybe a scalable system configured to efficiently manage resources andenhance security by provisioning computing resources in response totriggering events and terminating resources after completing a task(e.g., a scalable cloud service that spins up and terminates containerinstances).

FIG. 3 depicts an exemplary configuration of navigation-menu system 104.As will be appreciated by one skilled in the art, the components andarrangement of components included in navigation-menu system 104 mayvary. For example, as compared to the depiction in FIG. 3,navigation-menu system 104 may include a larger or smaller number ofprocessors, I/O devices, or memory units. In addition, navigation-menusystem 104 may further include other components or devices not depictedthat perform or assist in the performance of one or more processesconsistent with the disclosed embodiments. The components andarrangements shown in FIG. 3 are not intended to limit the disclosedembodiments, as the components used to implement the disclosed processesand features may vary.

Processor 310 may comprise known computing processors, including amicroprocessor. Processor 310 may constitute a single-core ormultiple-core processor that executes parallel processes simultaneously.For example, processor 310 may be a single-core processor configuredwith virtual processing technologies. In some embodiments, processor 310may use logical processors to simultaneously execute and controlmultiple processes. Processor 310 may implement virtual machinetechnologies, or other known technologies to provide the ability toexecute, control, run, manipulate, store, etc., multiple softwareprocesses, applications, programs, etc. In another embodiment, processor310 may include a multiple-core processor arrangement (e.g., dual core,quad core, etc.) configured to provide parallel processingfunctionalities to allow execution of multiple processes simultaneously.One of ordinary skill in the art would understand that other types ofprocessor arrangements could be implemented that provide for thecapabilities disclosed herein. The disclosed embodiments are not limitedto any type of processor(s) 310. Processor 310 may execute variousinstructions stored in memory 330 to perform various functions of thedisclosed embodiments described in greater detail below. Processor 310is configured to execute functions written in one or more knownprogramming languages.

I/O devices 320 may include at least one of a display, an LED, a router,a touchscreen, a keyboard, a microphone, a speaker, a haptic device, acamera, a button, a dial, a switch, a knob, a transceiver, an inputdevice, an output device, or another I/O device to perform methods ofthe disclosed embodiments. I/O devices 320 may be components of aninterface of navigation-menu system 106.

Memory 330 may be a volatile or non-volatile, magnetic, semiconductor,optical, removable, non-removable, or other type of storage device ortangible (i.e., non-transitory) computer-readable medium, consistentwith disclosed embodiments. As shown, memory 330 may include data 331,including one of at least one of encrypted data or unencrypted data.Consistent with disclosed embodiments, data 331 may include requestdata, model data (e.g., model parameters), activity data, account data,navigation-menu data, and/or navigation-menu rule data, consistent withdisclosed embodiments.

Programs 335 may include one or more programs (e.g., modules, code,scripts, or functions) used to perform methods consistent with disclosedembodiments. Programs may include operating systems (not shown) thatperform known operating system functions when executed by one or moreprocessors. Disclosed embodiments may operate and function with computersystems running any type of operating system. Programs 335 may bewritten in one or more programming or scripting languages. One or moreof such software sections or modules of memory 330 may be integratedinto a computer system, non-transitory computer-readable media, orexisting communications software. Programs 335 may also be implementedor replicated as firmware or circuit logic.

Programs 335 may include an account monitor 336, a machine-learningmodule 337, and a navigation-menu module 338, an authentication module339 and/or other modules not depicted to perform methods of thedisclosed embodiments. In some embodiments, modules of programs 335 maybe configured to generate (“spin up”) one or more ephemeral containerinstances to perform a task and/or to assign a task to a running (warm)container instance, consistent with disclosed embodiments. Modules ofprograms 335 may be configured to receive, retrieve, and/or generatemodels, consistent with disclosed embodiments. Modules of programs 335may be configured to perform operations in coordination with oneanother.

Account monitor 336 may include programs (scripts, functions,algorithms) to receive data from, retrieve data from, and/or transmitdata to other components of system 100 and/or computing componentsoutside system 100. For example, account monitor 336 may be configuredto receive data from user device 102, account system 106, and/orthird-party system 108. The data may include account data, activitydata, navigation-menu data, request data, or other data, consistent withdisclosed embodiments. For example, account monitor 336 may beconfigured to receive a request and retrieve account data from accountsystem 106 based on the request. Account monitor 336 may be configuredto send a request to third-party system 108 and receive activity data inresponse to the request.

In some embodiments, account monitor 336 may be configured to managedata (e.g., monitor, filter, classify, analyze, store, and/or providedata). In some embodiments, account monitor 336 may be configured tomanage request data, activity data, navigation-menu data, account data,and/or other data. The data may be associated with a user or an account,consistent with disclosed embodiments. In some embodiments, accountmonitor 336 may be configured to classify data as relating to one ormore accounts, the accounts being associated with one or more respectiveusers. Consistent with disclosed embodiments, account monitor 336 may beconfigured to train and implement machine learning models to analyzedata (e.g., in coordination with machine-learning module 337). Accountmonitor 336 may include algorithms or models to provide data statistics(e.g., means, variances, ranges, regression results, correlations,covariance, or other data statistics).

Machine-learning module 337 include programs (scripts, functions,algorithms) to train, implement, store, receive, retrieve, and/ortransmit one or more machine learning models. The machine learningmodels may include a neural network model, a recurrent neural network(RNN) model, a deep learning model, a random forest model, aconvolutional neural network (CNN) model, a support vector machine modeland/or another machine learning model. Models may include an ensemblemodel (i.e., a model comprised of a plurality of models). In someembodiments, training of a model terminates when a training criterion issatisfied. Training criterion may include a number of epochs, a trainingtime, a performance metric (e.g., an estimate of accuracy in reproducingtest data), or the like. Machine-learning module 337 may be configuredto adjust model parameters during training. Model parameters may includeweights, coefficients, offsets, or the like. Training may be supervisedor unsupervised.

Navigation-menu module 338 may include programs (scripts, functions,algorithms), to generate navigation menus. Generating a navigation menumay include selecting prompts to include in the navigation menu.Generating a navigation menu may include setting a configuration of thenavigation menu. Generating a navigation menu may be based on accountdata, activity data, request data, navigation-menu data, and/ornavigation-menu rule data, consistent with disclosed embodiments.

Consistent with disclosed embodiments, navigation-menu module 338 may beconfigured to train, implement, store, retrieve, and/or receive machinelearning models to generate navigation menus (e.g., in coordination withmachine-learning module 337). Models of navigation-menu module 338 maybe configured to generate navigation menus based on account data,activity data, navigation-menu data, and request data. Navigation-menumodule 338 may be configured to train a model to estimate the likelihooda prompt will be selected based on account data, activity data, and/ornavigation-menu data (e.g., in coordination with machine-learning module337). Navigation-menu module 338 may be configured to generatenavigation menus based on the estimated likelihood a prompt will beselected.

In some embodiments, navigation-menu module 338 may be configured toapply a navigation-menu rule to update a navigation menu generated by amodel. A navigation-menu rule may be any logical expression thatspecifies an element of a configuration of a navigation menu when acondition satisfied, consistent with disclosed embodiments.

Navigation-menu module 338 may be configured to receive instructions tomanage (i.e., to generate, modify, and/or delete) navigation-menu rulesfrom components of system 100 and/or from computing components outsidesystem 100. For example, navigation-menu module 338 may receiveinstructions to manage navigation-menu rules from user device 102.

Navigation-menu module 338 may include algorithms to generate datastatistics, i.e., navigation-menu module 338 may be configured toperform univariate statistical methods, multivariate statisticalmethods, regressions, or other statistical methods. In some embodiments,navigation-menu module 338 may be configured to determine a statisticalmetric and/or identify a structure (e.g., a data class, a key-valuepair, etc.) of account data, request data, activity data, and/ornavigation-menu data. Navigation-menu module 338 may be configured togenerate a model having a model type (e.g., an RNN model) based on anindication that a previous model of the type satisfied a performancecriterion when generating a navigation-menu using similar account data,activity data, and/or navigation.

Navigation-menu module 338 may include programs (e.g., scripts,functions, algorithms) to provide navigation menus. Providing anavigation menu may include storing navigation-menu data, transmittingnavigation-menu data (e.g., to a component of system 100 and/or acomputing component outside system 100), displaying navigation-menudata, playing a sound based on navigation-menu data, emitting lightbased on navigation-menu data, and/or the like.

In some embodiments, navigation-menu module 338 may be configured toprovide a prompt. Providing a prompt may include routing a signal. Asignal may include a call signal, an internet connection signal, and/orany other signal. In some embodiments, navigation-menu system 104 mayroute a signal based on request data, activity data, navigation-menudata, and/or a navigation-menu rule. For example, navigation-menu system104 may receive a call from a known phone number to a customer servicedepartment and route the call to a pharmacy department using a model,based on the phone number and account data indicating that aprescription is likely to have run out.

In some embodiments, navigation-menu module 338 may be configured totransmit data to conduct an IVR process in coordination withspeech-recognition system 112. In some embodiments, navigation-menumodule 338 may be configured to select a prompt based on received dataand/or generate a navigation menu based on received data. For example,navigation-menu 338 may transmit request data that includes audio datato speech-recognition system 112, receive text data based on the audiodata from speech-recognition system 112, and select a prompt based onthe received text data.

Authentication module 339 may be configured to conduct an authenticationprocess, consistent with disclosed embodiments. In some embodiments, anauthentication process includes transmitting a request to provide apassword, a pin, a token, an answer to a secret question, a code, abiometric input, or other authentication data. In some embodiments, anauthentication process includes receiving a password, a pin, an answerto a secret question, a code, a biometric input, a token, or otherauthentication data. In some embodiments, an authentication includes aCompletely Automated Public Turing Test to tell Computers and HumansApart (CAPTCHA) process. In some embodiments, an authentication processincludes a multi-factor authentication. In some embodiments, theauthentication process may be tokenized.

FIG. 4 depicts an exemplary user device 102, consistent with disclosedembodiments. User device 102 may be a phone, a mobile device, a tablet,a personal computer, a server, a server cluster, a specialized deviceconfigured to perform methods according to disclosed embodiments, or theany other user device.

User device 102 may include one or more processors 410, input/outputunits (I/O devices) 420, and one or more memory units 430. FIG. 4 is anexemplary configuration of user device 102. As will be appreciated byone skilled in the art, the components and arrangement of componentsincluded in user device 102 may vary. For example, as compared to thedepiction in FIG. 4, user device 102 may include a larger or smallernumber of processors 410, I/O devices 420, or memory units 430. Inaddition, user device 102 may further include other components ordevices not depicted that perform or assist in the performance of one ormore processes consistent with the disclosed embodiments. The componentsand arrangements shown in FIG. 4 are not intended to limit the disclosedembodiments, as the components used to implement the disclosed processesand features may vary.

Processor 410 may include known computing processors, including amicroprocessor. Processor 410 may include a single-core or multiple-coreprocessor that executes parallel processes simultaneously. For example,processor 410 may include a single-core processor configured withvirtual processing technologies. In some embodiments, processor 410 mayuse logical processors to simultaneously execute and control multipleprocesses. Processor 410 may implement virtual machine technologies, orother known technologies to provide the ability to execute, control,run, manipulate, store, etc., multiple software processes, applications,programs, etc. In another embodiment, processor 410 may include amultiple-core processor arrangement (e.g., dual core, quad core, etc.)configured to provide parallel processing functionalities to allowexecution of multiple processes simultaneously. One of ordinary skill inthe art would understand that other types of processor arrangements maybe implemented that provide for the capabilities disclosed herein. Thedisclosed embodiments are not limited to any type of processor(s) 410.Processor 410 may execute various instructions stored in memory 430 toperform various functions of the disclosed embodiments described ingreater detail below. Processor 410 may be configured to executefunctions written in one or more known programming languages.

Referring again to FIG. 4, I/O devices 420 may include components of aninterface, such as a user interface. I/O devices 420 may include amicrophone 421, a speaker 422, an input device 423, a display 424, atransceiver 425, haptic device 426, and/or sensor 427. I/O devices 420may include other I/O devices, not depicted, that perform or assist inthe performance of one or more processes consistent with disclosedembodiments. In some embodiments, some or all of I/O devices 420 may bemounted to user device 102. In some embodiments, some or all of I/Odevices 420 may be components of stand-alone devices communicativelycoupled to user device 102.

Microphone 421 may be configured to receive an audio signal. In someembodiments, microphone 421 may include a microphone array. Microphone421 may be mounted to user device 102 or may be communicatively coupledto user device 102 (e.g., a wired headset, wireless microphone, or thelike).

Speaker 422 may include components configured to provide audio output.In some embodiments, speaker 422 may include an array of speakers.

Input device 423 may include at least one of a touchpad, a touch screen,a keyboard, a mouse, a track pad, a button, a dial, a knob, a switch, alocation sensor, a biometric input device, or the any other inputdevice. As will be appreciated by one of skill in the art, input device423 may include any device capable of receiving inputs to perform orassist in performing methods consistent with disclosed embodiments.

Display 424 may include a light-emitting component, such as a lightemitting diode (LED) or other component capable of providing a visiblesignal to a user. In some embodiments, display 424 may include at leastone of a monitor, an LCD display, an LED display, a touch screen, alamp, a projector, or another visual display.

Transceiver 425 may include a transceiver configured to connect with atleast one of any type of cellular data network, or at least one of aWi-Fi transceiver, a Li-Fi transceiver, Near Field Communication (NFC)transceiver, a radio transceiver, an ultra-high frequency (UHF)transceiver, a Bluetooth transceiver, an infrared transceiver, or otherwireless transceiver.

Haptic device 426 may be configured to provide haptic feedback. Forexample, haptic device 426 may include a device configured to vibrate(e.g., an eccentric rotating mass actuator and/or a linear resonantactuator), to provide force, or to induce a sense of touch withoutphysical contact of a device (e.g., via an air vortex ring, ultrasound,etc.).

Sensor 427 may include, for example, a location sensor (e.g., a globalpositioning system (GPS) sensor, a magnetometer, or an altimeter), acamera, a light sensor, an audio sensor, or a motion sensor (e.g., agyroscope, an accelerometer, a light-based motion detector).

Memory 430 may be a volatile or non-volatile, magnetic, semiconductor,optical, removable, non-removable, or other type of storage device ortangible (i.e., non-transitory) computer-readable medium, consistentwith disclosed embodiments. As shown, memory 430 may include data 431,including of at least one of encrypted data or unencrypted data. Data431 may include one or more model indexes, model parameters, modelcodes, data indexes, data vectors, and/or datasets, consistent withdisclosed embodiments.

Programs 435 may include one or more programs (e.g., modules, code,scripts, or functions) used to perform methods consistent with disclosedembodiments. Programs may include operating systems (not shown) thatperform known operating system functions when executed by one or moreprocessors. Disclosed embodiments may operate and function with computersystems running any type of operating system. Programs 435 may bewritten in one or more programming or scripting languages. One or moreof such software sections or modules of memory 430 may be integratedinto a computer system, non-transitory computer-readable media, orexisting communications software. Programs 435 may also be implementedor replicated as firmware or circuit logic.

In some embodiments, programs 435 includes one or more programs to tomonitor service requests. For example, programs 435 may be configured tostore request data, account data, navigation-menu data, and/or activitydata, consistent with disclosed embodiments. Programs 435 may beconfigured to transmit request data, account data, navigation-menu data,and/or activity data to other components of system 100. Programs 435 maybe configured to manage navigation-menu rules, as described above (inreference to navigation-menu module 338).

FIG. 5 depicts exemplary process 500 for training a model to generate anavigation menu, consistent with disclosed embodiments. In someembodiments, navigation-menu system 104 may perform one or more steps ofprocess 500 using programs 335. One or more of account monitor 336,machine-learning module 337, navigation-menu module 338, authenticationmodule 339, and/or another module of programs 335 may perform operationsof process 500, consistent with disclosed embodiments. In someembodiments, account system 106 may perform steps of process 500. Insome embodiments, user device 102 may perform steps of process 500. Insome embodiments, speech-recognition system 112 may perform steps ofprocess 500.

Consistent with disclosed embodiments, steps of process 500 may beperformed on one or more cloud services using one or more ephemeralcontainer instances. For example, at any of the steps of process 500,navigation-menu system 104, account system 106, and/orspeech-recognition system 112 may generate (spin up) an ephemeralcontainer instance to execute a task, assign a task to analready-running ephemeral container instance (warm container instance),or terminate a container instance upon completion of a task. As one ofskill in the art will appreciate, steps of process 500 may be performedas part of an application interface (API) call. Process 500 may beperformed according to a schedule and/or in response to a triggeringevent (e.g., a received request to generate a model).

At step 502, navigation-menu system 104 may receive account dataassociated with a plurality of accounts, consistent with disclosedembodiments. In some embodiments, step 502 may include retrieving orreceiving account data from account system 106, data 331, database 110,and/or a computing component outside system 100. Account data at step502 may be associated with one or more users. Account data of step 502may include any type of account data as previously described and/orother account data.

At step 504, navigation-menu system 104 may receive request data for aplurality of requests associated with a plurality of accounts,consistent with disclosed embodiments. In some embodiments, the requestdata may be associated with one or more users. In some embodiments, step504 may include retrieving or receiving request data from account system106, data 331, database 110, and/or a computing component outside system100. Request data of step 504 may include any type of request data aspreviously described or other request data. Step 504 may includetransmitting data to and receiving data from speech-recognition module112, consistent with disclosed embodiments.

At step 506, navigation-menu system may 104 receive activity dataassociated with one or more users and/or accounts, consistent withdisclosed embodiments. In some embodiments, step 506 may includeretrieving or receiving activity data from third-party system 108, data331, database 110, and/or a computing component outside system 100.Activity data of step 506 may include any type of activity data aspreviously described and/or other activity data.

At step 508, navigation-menu system 104 may receive navigation-menu datafor a plurality of requests associated with a plurality of accounts,consistent with disclosed embodiments. In some embodiments, step 508 mayinclude retrieving or receiving navigation-menu data from account system106, data 331, database 110, and/or a computing component outside system100. Navigation-menu data of step 508 may include any navigation-menudata as previously described and/or any other navigation-menu data.

At step 510, navigation-menu system 104 may generate a model, consistentwith disclosed embodiments. The model may be a machine-learning model.For example, the model may include a neural network model, a recurrentneural network model, a deep learning model, a random forest model, aconvolutional neural network model, and/or another machine-learningmodel. Navigation-menu system 104 may generate a model based on apreviously generated model. For example, navigation-menu system 104 mayreceive or retrieve a model from a data storage (e.g., data 331 and/ordatabase 110), another component of system 100, or a computing componentoutside system 100 (e.g., a database). In some embodiments, thegenerated model of step 510 may be a copy of a previously generatedmodel.

Navigation-menu system 104 may generate a model at step 510 based onaccount data, activity data, and/or navigation-menu data. For example,navigation-menu system 104 may determine a statistical metric and/oridentify a structure of account data, request data, activity data,and/or navigation-menu data. Navigation-menu system 104 may generate amodel having a model type (e.g., RNN, CNN, random forest, or other modeltype) based on an indication that a previous model of the type satisfieda performance criterion when generating a navigation-menu using similaraccount data, activity data, and/or navigation. The model may be anensemble model. In some embodiments, navigation-menu system 104generates a plurality of models at step 510.

At step 512, navigation-menu system 104 may train a model to generate anavigation menu, consistent with disclosed embodiments. Generating anavigation menu may include selecting prompts to include in thenavigation menu. Generating a navigation menu may include setting aconfiguration of the navigation menu. Consistent with disclosedembodiments, the navigation menu may have a configuration of prompts andinclude any navigation-menu data as previously described (e.g., thenavigation menu may be an IVR menu).

Navigation-menu system 104 may train the model to estimate thelikelihood a prompt will be selected following a service request. Themodel may be trained to estimate the likelihood based on account data,activity data, and/or navigation-menu data. Navigation-menu system 104may train a model to generate and/or configure (e.g. order) navigationmenus based on the estimated likelihood a prompt will be selected. Insome embodiments, at step 512, navigation-menu system 104 may train amodel to estimate the likelihood a prompt will be selected and togenerate navigation menus. In some embodiments, at step 512,navigation-menu system 104 may train a first model to estimate thelikelihood a prompt will be selected and a second model to generatenavigation menus.

In some embodiments, training of a model may terminate at step 512 whena training criterion is satisfied. Training criteria may include anumber of epochs, a training time, a performance metric (e.g., anestimate of accuracy in predicting the selected prompt), or the like.Step 512 may include adjusting model parameters during training. Modelparameters may include weights, coefficients, offsets, or the like.Training at step 512 may be supervised or unsupervised.

At step 514, navigation-menu system 104 may provide a model, consistentwith disclosed embodiments. Providing a model at step 514 may includestoring the model (e.g., in data 331 and/or database 110), transmittingthe model to a component of system 100, transmitting the model to acomputing component outside system 100 (e.g., via network 114), and/ordisplaying the model (e.g., at display of I/O 320). In some embodiments,at step 514, navigation-menu system 104 may provide a first model toestimate the likelihood a prompt will be selected and a second model togenerate navigation menus. In some embodiments, at step 514,navigation-menu system 104 may provide one model to estimate thelikelihood a prompt will be selected and generate navigation menus. Themodel may be an ensemble model.

FIG. 6 depicts exemplary process 600 for generating a navigation menu,consistent with disclosed embodiments. In some embodiments,navigation-menu system 104 may perform one or more steps of process 600using programs 335. One or more of account monitor 336, machine-learningmodule 337, navigation-menu module 338 authentication module 339, and/oranother module of programs 335 may perform operations of process 600,consistent with disclosed embodiments. In some embodiments, accountsystem 106 may perform steps of process 600. In some embodiments, userdevice 102 may perform steps of process 600. In some embodiments,speech-recognition system 112 may perform steps of process 600.

Consistent with disclosed embodiments, steps of process 600 may beperformed on one or more cloud services using one or more ephemeralcontainer instances. For example, at any of the steps of process 600,navigation-menu system 104, account system 106, and/orspeech-recognition system 112 may generate (spin up) an ephemeralcontainer instance to execute a task, assign a task to analready-running ephemeral container instance (warm container instance),or terminate a container instance upon completion of a task. As one ofskill in the art will appreciate, steps of process 600 may be performedas part of an application interface (API) call.

At step 602, navigation-menu system 104 may receive request data,consistent with disclosed embodiments. For example, navigation-menusystem 104 may receive request data from account system 106 and/or userdevice 102. In some embodiments, user device 102 may transmit a requestto account system 106 and account system 106 may transmit request dataassociated with the request to navigation-menu system 104. In someembodiments, user device 102 may transmit a request to navigation-menusystem 104, and navigation-menu system 104 may generate request dataand/or receive request data associated with the request. Request data ofstep 602 may include any request data as previously described and/orother request data (e.g., IVR data comprising a response to a prompt).

At step 604, navigation-menu system 104 may receive account data basedon the request, consistent with disclosed embodiments. Based on therequest, navigation-menu system 104 may retrieve or receive account datafrom account system 106, data 331, database 110, and/or a computingcomponent outside system 100. Account data of step 604 may include anyaccount data as previously described and/or other account data.

At step 606, navigation-menu system 104 may receive activity dataassociated with the account, consistent with disclosed embodiments. Insome embodiments, navigation-menu system 104 may retrieve or receiveactivity data from third-party system 108, data 331, database 110,and/or a computing component outside system 100. In some embodiments,the activity data may include at least one of location data, cookiedata, website history data, viewing history data (e.g., online videostreaming activity), shopping history data, transaction data, internetsearch data, social media data, or other activity data.

At step 608, navigation-menu system 104 may receive a model, consistentwith disclosed embodiments. In some embodiments, step 608 may includeimplementing process 500 to generate and train a model. In someembodiments, step 608 may include generating a statistical metric and/oridentifying a structure of account data, request data, and/or activitydata. In some embodiments, step 608 may include selecting a previouslytrained model based on the statistical metric and/or structure of theaccount data, request data, and/or activity data.

At step 610, navigation-menu system 104 may generate a navigation menuusing the model, consistent with disclosed embodiments. Generating anavigation menu may include selecting prompts to include in thenavigation menu. Generating a navigation menu may include setting aconfiguration of the navigation menu. A navigation menu of step 610 mayinclude any navigation menu as previously described and/or anothernavigation menu (e.g., an IVR menu). Navigation-menu system 104 maygenerate a navigation menu at step 610 using the model based on requestdata, account data, activity data, and/or a navigation-menu rule,consistent with disclosed embodiments. A navigation-menu rule of step610 may include any navigation-menu rule as previously described and/oranother navigation-menu rule. Generating a navigation menu may includetransmitting data to and receiving data from speech-recognition module112, consistent with disclosed embodiments.

At step 612, navigation-menu system 104 may provide the navigation menu,consistent with disclosed embodiments. Providing a navigation menu mayinclude storing a navigation menu, transmitting a navigation menu to acomponent of system 100, transmitting a navigation menu to a computingcomponent outside system 100 (e.g., via network 114), displaying anavigation menu, playing a sound, emitting light, and the like. In someembodiments, providing a navigation menu may include providing a prompt.In some embodiments, providing a prompt may include routing a signal(e.g., a call signal, an internet connection signal, and/or any othersignal).

At step 614, navigation-menu system 104 may receive input data,consistent with disclosed embodiments. In some embodiments, the inputmay be received from user device 102 and/or account system 106. In someembodiments, input data may include navigation-menu data (e.g., a promptselection). In some embodiments, input data may include request data.For example, input data may include information relating to a servicerequest such as a desired outcome, a scheduling preference, datarelating to an account event, or the like. Receiving input data mayinclude transmitting data to and receiving input data fromspeech-recognition module 112, consistent with disclosed embodiments.

As shown in FIG. 6, steps 608 to 614 may be repeated one or more times.For example, based on the user input of step 614, navigation menu system104 may receive a model and/or generate a different navigation menuusing the model (steps 608 and 610).

At step 616, navigation-menu system 104 may update the model based onthe user input, consistent with disclosed embodiments. For example, theuser input may include navigation-menu data, and navigation-menu system104 may train the model may be trained using the navigation-menu data.

At step 618, navigation-menu system 104 may store the model, consistentwith disclosed embodiments. For example, navigation-menu system 104 maystore the model in data 331 and/or database 110. In some embodiments,step 618 include, transmitting the model to a component of system 100and/or transmitting the model to a computing component outside system100.

FIG. 7 depicts exemplary process 700 to view, create, update, or deletea navigation-menu rule, consistent with disclosed embodiments.Consistent with disclosed embodiments, a navigation-menu rule may be anylogical expression that specifies an element of a configuration of anavigation menu when a condition satisfied. In some embodiments,navigation-menu system 104 may perform one or more steps of process 700using programs 335. One or more of account monitor 336, machine-learningmodule 337, navigation-menu module 338, authentication module 339,and/or another module of programs 335 may perform operations of process700, consistent with disclosed embodiments. In some embodiments, accountsystem 106 may perform steps of process 700.

At step 702, navigation-menu system 104 may receive a request,consistent with disclosed embodiments. In some embodiments, the requestmay be received from a user device (e.g., user device 102). In someembodiments, the request may include a request to view, create, update,or delete navigation-menu rules.

At step 704, navigation-menu system 104 may conduct an authenticationprocess, consistent with disclosed embodiments. In some embodiments, theauthentication process may include transmitting a request to provide apassword, a pin, a token, an answer to a secret question, a code, abiometric input, or other authentication data. In some embodiments, theprocess may include receiving a password, a pin, an answer to a secretquestion, a code, a biometric input, a token, or other authenticationdata. In some embodiments, the authentication may include a CompletelyAutomated Public Turing Test to tell Computers and Humans Apart(CAPTCHA) process. In some embodiments, the authentication process mayinclude a multi-factor authentication. In some embodiments, theauthentication process may be tokenized. Conducting an authenticationprocess at step 704 may include transmitting data to and receiving datafrom speech-recognition module 112, consistent with disclosedembodiments.

At step 706, navigation-menu system 104 may access navigation-menu rulesbased on the authentication process, consistent with disclosedembodiments. For example, navigation-menu system may receive and/orretrieve navigation-menu rules from a data storage (e.g., data 331and/or database 110) and/or account system 106.

At step 708, navigation-menu system 104 may receive instructions,consistent with disclosed embodiments. In some embodiments, theinstructions may include an instruction to create, modify, or delete anavigation menu rule. In some embodiments, the instructions are receivedas part of the request of step 702. Step 708 may include transmittingdata to and receiving data from speech-recognition module 112,consistent with disclosed embodiments.

At step 710, navigation-menu system 104 may store a navigation-menurule, consistent with disclosed embodiments. In some embodiments, step710 may include storing a navigation-menu rule includes in a datastorage (e.g., data 331 and/or database 110). In some embodiments, step710 may include transmitting a navigation-menu rule to account system106 and/or a user device.

FIG. 8 depicts an exemplary process 800 for activating a user account,consistent with disclosed embodiments. In some embodiments, a userdevice 102 may perform steps of process 800, consistent with disclosedembodiments. In some embodiments, account system 106 may perform stepsof process 800.

At step 802, user device 102 may conduct an authentication process,consistent with disclosed embodiments. The authentication process mayinclude an authentication process as previously described and/or anyother authentication process.

At step 804, the user device 102 may manage a navigation-menu rule basedon the authentication process, consistent with disclosed embodiments.Managing a navigation-menu rule may include providing instructions tocreate, modify, and/or delete a navigation-menu rule.

At step 806, user device 102 may monitor data, consistent with disclosedembodiments. Monitoring data at step 806 may include monitoring requestdata, activity data, account data, and/or navigation-menu data.Monitoring data may include collecting, storing, and/or analyzing data.Monitoring data may include collecting data using any of I/O devices 420(e.g., microphone 421, input device 423, sensor 427, or another I/Odevice).

At step 808, user device 102 may provide data, consistent with disclosedembodiments. Providing data at step 808 may include transmitting requestdata, activity data, account data, and/or navigation-menu data toanother component of system 100.

At step 810, user device 102 may transmit request data associated with arequest for an account service to at least one of navigation-menu system104 or account system 106. Transmitting request data may include placinga call, logging into an account, visiting a website, sending a message(e.g., an email, a text message, a chat message, or the like). Requestdata of step 810 may include any type of request data as previouslydescribed or other request data.

At step 812, user device 102 may receive prompt data, consistent withdisclosed embodiments. Step 812 may include receiving a navigation menu,consistent with disclosed embodiments. Receiving prompt data at step 812may include receiving prompt data from navigation-menu system 104 and/oraccount system 106. In some embodiments, navigation menu system 104 mayperform steps of process 600 based on the request transmitted at step810 and transmit prompt data to the user device at step 812

At step 814 user device 102 may display or play prompt data, consistentwith disclosed embodiments. For example, the user device may displayprompt data using speaker 422. The user device may display prompt datausing display 424. In some embodiments, step 814 may include providinghaptic feedback via haptic device 426.

At step 816 user device 102 may receive input data, consistent withdisclosed embodiments. In some embodiments, step 816 may includereceiving input from microphone 421, input device 423, and/or sensor427. In some embodiments, input data may include navigation-menu data(e.g., a prompt selection). In some embodiments, input data may includerequest data. For example, input data may include information relatingto a service request such as a desired outcome, a scheduling preference,data relating to an account event, or the like.

At step 818 user device 102 may transmit input data to at least one ofnavigation-menu system 104 or account system 106, consistent withdisclosed embodiments.

As shown in FIG. 8, steps 812 to 818 may be repeated one or more times.For example, based on the user input (step 816), navigation menu system104 may provide additional or different prompt data (step 812). In someembodiments, navigation menu system 104 may perform steps of process 600based on the input data transmitted at step 818.

It should be noted that account system 106 may be configured to performany of the steps described in reference to navigation-menu system 104and/or user device 102 including, for example, steps of process 500,600, 700, and 800. In some embodiments, navigation-menu system 104 mayprovide a model to account system 106, and account system 106 may trainand/or implement the model to generate and provide navigation menus. Insome embodiments, account system 106 may be configured to train andimplement a model to generate and/or to provide navigation menus.

Systems and methods disclosed herein involve unconventional improvementsover conventional data processing approaches. Descriptions of thedisclosed embodiments are not exhaustive and are not limited to theprecise forms or embodiments disclosed. Modifications and adaptations ofthe embodiments will be apparent from consideration of the specificationand practice of the disclosed embodiments. Additionally, the disclosedembodiments are not limited to the examples discussed herein.

The foregoing description has been presented for purposes ofillustration. It is not exhaustive and is not limited to the preciseforms or embodiments disclosed. Modifications and adaptations of theembodiments will be apparent from consideration of the specification andpractice of the disclosed embodiments. For example, the describedimplementations include hardware and software, but systems and methodsconsistent with the present disclosure can be implemented as hardwarealone.

Computer programs based on the written description and methods of thisspecification are within the skill of a software developer. The variousfunctions, scripts, programs, or modules can be created using a varietyof programming techniques. For example, programs, scripts, functions,program sections or program modules can be designed in or by means oflanguages, including JAVASCRIPT, C, C++, JAVA, PHP, PYTHON, RUBY, PERL,BASH, or other programming or scripting languages. One or more of suchsoftware sections or modules can be integrated into a computer system,non-transitory computer-readable media, or existing communicationssoftware. The programs, modules, or code can also be implemented orreplicated as firmware or circuit logic.

Moreover, while illustrative embodiments have been described herein, thescope includes any and all embodiments having equivalent elements,modifications, omissions, combinations (e.g., of aspects across variousembodiments), adaptations or alterations based on the presentdisclosure. The elements in the claims are to be interpreted broadlybased on the language employed in the claims and not limited to examplesdescribed in the present specification or during the prosecution of theapplication, which examples are to be construed as non-exclusive.Further, the steps of the disclosed methods can be modified in anymanner, including by reordering steps or inserting or deleting steps. Itis intended, therefore, that the specification and examples beconsidered as exemplary only, with a true scope and spirit beingindicated by the following claims and their full scope of equivalents.

1-20. (canceled)
 21. A non-transitory computer-accessible medium havingstored thereon computer-executable instructions, wherein, when acomputer arrangement comprising a processor executes the instructions,the computer arrangement is configured to: receive navigation-menu datacomprising Interactive Voice Response data and prompt data, wherein thenavigation menu-data specifies a configuration of one or more promptsand prompt data; train a model based on the navigation-menu data;receive a signal and user-account data; generate, using the model, anavigation menu based on the signal and the user-account data, whereingenerating the navigation menu comprises setting the configuration ofthe navigation menu based on likelihoods associated with prompts of thenavigation menu; route the signal; provide the prompts of the navigationmenu; receive a selection of a prompt of the navigation menu; generate,using the model, an updated navigation menu based on the selectedprompt; and provide the updated navigation menu.
 22. The non-transitorycomputer-accessible medium of claim 21, wherein the computer arrangementis configured to: receive account data associated with a plurality ofaccounts, the account data comprising at least one selected from thegroup of account status data, account event data, and transaction data;and train the model based on the account data.
 23. The non-transitorycomputer-accessible medium of claim 22, wherein: the operations furthercomprise receiving activity data associated with the plurality ofaccounts; and training the model based on the activity data.
 24. Thenon-transitory computer-accessible medium of claim 23, wherein: theoperations further comprise receiving user-activity data; and generatingthe navigation menu is based on the user-activity data.
 25. Thenon-transitory computer-accessible medium of claim 21, wherein thecomputer arrangement is configured to: apply speech recognition model tothe call signal; and convert recognized speech to text.
 26. Thenon-transitory computer-accessible medium of claim 21, wherein thecomputer arrangement is configured to: associate the call signal with atleast one selected from the group of a residence and a place ofbusiness; and train the model to generate the navigation menu based onthe call signal.
 27. The non-transitory computer-accessible medium ofclaim 21, wherein: the navigation-menu data being associated with theaccounts, and the prompt data includes at least one selected from thegroup of audio data, text data, numeric data, image data, video data,and Interactive Voice Response data.
 28. The non-transitorycomputer-accessible medium of claim 21, wherein the signal comprisesrequest data and at least one selected from the group of a call signaland an internet connection signal.
 29. The non-transitorycomputer-accessible medium of claim 28, wherein the request datacomprises at least one selected from the group of a phone number, aninternet protocol address, location data, time data, anduser-satisfaction data.
 30. The non-transitory computer-accessiblemedium of claim 28, wherein the request data comprises at least onerequest for service selected from the group of a request to check anaccount status, a request to schedule an event, a request to reportfraud, a request to change an address, and a request to determinewhether an anticipated account event occurred.
 31. The non-transitorycomputer-accessible medium of claim 21, wherein the computer arrangementis configured to adjust a model parameter during training of the model,wherein the model parameter comprises at least one selected from thegroup of a weight, a coefficient, and an offset.
 32. A system,comprising: a server comprising a processor and a memory, wherein, uponreceipt of a signal and user-account data associated, the server isconfigured to: receive navigation-menu data comprising Interactive VoiceResponse data and prompt data, wherein the navigation menu-dataspecifies a configuration of one or more prompts and prompt data; traina model based on the navigation-menu data; generate, using the model, anavigation menu based on the signal and the user-account data, whereingenerating the navigation menu comprises setting the configuration ofthe navigation menu based on likelihoods associated with prompts of thenavigation menu; route the signal; provide the prompts of the navigationmenu; receive a selection of a prompt of the navigation menu; generate,using the model, an updated navigation menu based on the selectedprompt; and provide the updated navigation menu.
 33. The server of claim32, wherein: the navigation menu organizes the prompts in a plurality oflevels, and each level includes a plurality of prompts in a specifiedorder.
 34. The server of claim 32, wherein the signal comprises requestdata and at least one selected from the group of a call signal and aninternet connection signal.
 35. The server of claim 34, wherein therequest data comprises at least one selected from the group of a phonenumber, an internet protocol address, location data, time data, anduser-satisfaction data.
 36. The server of claim 34, wherein the requestdata comprises at least one request for service selected from the groupof a request to check an account status, a request to schedule an event,a request to report fraud, a request to change an address, and a requestto determine whether an anticipated account event occurred.
 37. Theserver of claim 32, wherein the server is configured to: receive accountdata associated with a plurality of accounts, the account datacomprising at least one selected from the group of account status data,account event data, and transaction data; and train the model based onthe account data.
 38. A method for generating a navigation menucomprising, the following operations performed by a server: receivenavigation-menu data comprising Interactive Voice Response data andprompt data, wherein the navigation menu-data specifies a configurationof one or more prompts and prompt data; train a model based on thenavigation-menu data; receive a signal and user-account data; generate,using the model, a navigation menu based on the signal and theuser-account data, wherein generating the navigation menu comprisessetting the configuration of the navigation menu based on likelihoodsassociated with prompts of the navigation menu; route the signal;provide the prompts of the navigation menu; receive a selection of aprompt of the navigation menu; generate, using the model, an updatednavigation menu based on the selected prompt; and provide the updatednavigation menu.
 39. The method of claim 38, wherein the signalcomprises request data and at least one selected from the group of acall signal and an internet connection signal.
 40. The method of claim39, wherein the request data comprises at least one selected from thegroup of a phone number, an internet protocol address, location data,time data, and user-satisfaction data.